POS0129 DIFFERENTIALLY EXPRESSED GENES (DEGS) IN PATIENTS WITH DERMATOMYOSITIS AND JUVENILE DERMATOMYOSITIS
Authors: R. Martinez, C. Elizondo Solis, A. Montoya Rosales, M. Salinas Carmona, N. Macias-Segura
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Dermatomyositis (DM) and Juvenile Dermatomyositis (JDM) are englobed in the spectrum of inflammatory myopathies. These are autoimmune diseases that cause heliotropic rash in most patients, which then develop proximal muscles weakness and skin lesions [1]. Key findings in patients with these pathologies include anti-Mi2, anti-Jo1, and anti-P155/P140 antibodies in plasma, as well as B-cells and plasma cells infiltrates with an upregulated Type I Interferon (IFN) signature in muscle biopsies [2]. However, the pathophysiology underlying this condition has not been fully comprehended; even less, diagnostic biomarkers that could identify early stages of the disease. <h3>Objectives</h3> We aimed to identify overlapped genes across datasets of gene expression from patients with DM and JDM doing an integrative bioinformatics analysis. <h3>Methods</h3> A search strategy was designed in the Gene Expression Omnibus platform to identify datasets of gene expression profiling by array experiments in patients with DM and JDM. The inclusion criteria were: 1) Assays performed in humans, 2) Inclusion of healthy controls in the datasets, and 3) Data analysis with GEO2R. The exclusion criteria were 1) Incomplete information of the genes, and 2) Gene expression by RNAseq. DEGs were selected when p &lt; 0.05 and log fold change &gt; 2 or &lt; -2. Functional enrichment analysis was realized with free access platforms GeneMania and DAVID with the significant overlapped genes. A protein-protein interaction (PPI) analysis was constructed with STRING and a topological interaction network using CytoHubba to identify genes with the strongest interactions. A prediction of transcription factors that can serve as possible therapeutic targets was realized with iRegulon. <h3>Results</h3> 102 overlapped DEGs from DM and 13 overlapped DEGs from JDM were found within their datasets. Out of those DEGs, 5 were shared between DM and JDM. Prediction analysis yielded 20 co-expressed genes. DAVID analysis showed the top 3 biological processes in which the 25 genes are involved, most of them belonging to the Type I IFN signature (Table 1). Topological analysis yielded 10 genes with the strongest interactions between them, and the transcription factor listed as possible therapeutic target was STAT1. <h3>Conclusion</h3> We aimed to identify DEGs that participate in the pathophysiology of DM and JDM. Those genes mostly participate in responses to viral infections, in which the type I IFN signature is present. These genes may serve in the future as possible biomarkers for early diagnosis. An <i>in vivo</i> analysis in patients with these diseases is needed to validate these findings. Janus Kinase Inhibitors (Jakinibs) have been used to treat JDM patients, showing positive results [3]. We suggest more clinical trials to test the effectiveness and tolerability of Jakinibs to treat these conditions. <h3>References</h3> [1]Schmidt, Jens. "Current Classification and Management of Inflammatory Myopathies." Journal of neuromuscular diseases vol. 5,2 (2018): 109-129. doi:10.3233/JND-180308 [2]Satoh, Minoru et al. "A Comprehensive Overview on Myositis-Specific Antibodies: New and Old Biomarkers in Idiopathic Inflammatory Myopathy." Clinical reviews in allergy &amp; immunology vol. 52,1 (2017): 1-19. doi:10.1007/s12016-015-8510-y [3]Le Voyer, Tom et al. "JAK inhibitors are effective in a subset of patients with juvenile dermatomyositis: a monocentric retrospective study." Rheumatology (Oxford, England) vol. 60,12 (2021): 5801-5808. doi:10.1093/rheumatology/keab116 <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.